Publications

Ion Beam Induced Deposition (IBID) is employed to fabricate three-dimensional nanoprotrusions on top of the recording pads of an active pixel sensor array (APS-MEA) featuring 4096 microelectrodes. Modified APS-MEAs are envisioned as enhanced tools to achieve real-time “in-cell” recordings from thousands of sensing elements, thus aiming to large-scale in-vitro registrations with unprecedented signal quality. A generalized electric model is proposed to address the revealed complexity of the neuron/electrode interface, and simulations have been conducted revealing the most advantageous cell/electrode coupling conditions. Preliminary results on the recording of spontaneous activity in cultured neuronal networks by means of nanostructured microelectrodes demonstrate the compatibility of IBID technology and APS-MEA infrastructure. The interface between cultured mammalian neurons and modified microelectrodes is revealed by FIB/SEM analysis, fostering the employment of the proposed electrical model for interpretation of electrical recordings from nanostructured microelectrodes.

It is well proven that the functional electrophysiological behavior of in-vitro neuronal networks is influenced by the structural connectivity. Thus, the automatic extraction of the topology in large assemblies of interconnected neurons can be a valuable tool for investigating the basic mechanisms underlying high-level cognitive functions. In this paper we propose a method for estimating the structural connectivity of neuronal networks from multimodal datasets combining high-resolution Multi-Electrode Arrays (MEA) and fluorescence microscopy. Probabilistic directional features are used in a graph heat kernel framework to identify the structural connectivity of the neuronal network. Electrode connectivity maps are computed as weighted graphs in which the edge weights represent the strength of the structural connection.

Multielectrode arrays (MEAs) are extensively used for electrophysiological studies on brain slices, but the spatial resolution and field of recording of conventional arrays are limited by the low number of electrodes available. Here, we present a large-scale array recording simultaneously from 4096 electrodes used to study propagating spontaneous and evoked network activity in acute murine cortico-hippocampal brain slices at unprecedented spatial and temporal resolution. We demonstrate that multiple chemically induced epileptiform episodes in the mouse cortex and hippocampus can be classified according to their spatio-temporal dynamics. Additionally, the large-scale and high-density features of our recording system enable the topological localization and quantification of the effects of antiepileptic drugs in local neuronal microcircuits, based on the distinct field potential propagation patterns. This novel high-resolution approach paves the way to detailed electrophysiological studies in brain circuits spanning spatial scales from single neurons up to the entire slice network.

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High density MEAs, providing recording capability from thousand of electrodes, are nowadays a commercial tool (www.3brain.com) used to investigate spontaneous or chemically modulated activity in dissociated cultures as well as ex vivo brain tissues. In previous paper we already demonstrated that such as high spatial resolution (4’096 electrodes, 42 um pitch on a 2.7 mm by 2.7 mm active area) allows to infer the functional connectivity of low density cultures both at global network level and at the resolution of microcircuits of few cells, showing that functional connectivity qualitatively maps the topological and morphological spatial distribution of the network (Maccione et al., 2012). As a further step to better understand how information is processed in neuronal networks, here we present preliminary results on effective connectivity obtained by electrically stimulating dissociate cultures using a new generation of high density devices that integrate stimulating capabilities. These innovative MEAs provide 4’096 recording electrodes with a pitch of 81 um (active area of 8 mm by 8 mm) interlaced with stimulating electrodes every 8 recording sites. We applied on dissociated hippocampal networks at 18-21 DIVs a train of biphasic signals (3 Volt peak to peak, duty cycle 500 us) at low frequency (0.2 Hz) for each of the 16 stimulating sites, inducing repetitive and reliable responses.The analysis of the resulting Post Stimulus Time Histograms (PSTHs) show interesting features. First, the “early response” just after the deliver of the stimulus as observed on conventional MEAs, considered to be a non sinaptically propagated response (Wagenaar et al., 2005), is almost absent. Second, the PSTH center of mass changes according to the distance from the stimulation site. Interestingly, the induced propagating patterns are reliable and situ dependent, showing that the stimulation in different areas is able to elicit specific responses not belonging to the repertoire of spontaneous bursting activity.As a perspective, these results suggest that the stimulating capability of high density MEA combined with optical imaging might be a valuable tool to reconstruct the effective connectivity, opening thus new perspectives in understanding network signal processing.

Homeostatic plasticity is one of the key mechanisms ensuring the remarkable adaptive abilities of the brain. However, this is still a relatively scantly explored branch of both experimental and computational neuroscience - in particular on a large, multi-neuronal scale. With recent advance in recording techniques, the lack of experimental data can be easily overcome – novel multielectrode arrays allow for high-density recordings from in vitro cultures consisting of thousands of neurons. What is needed to complement this rich data is analysis techniques that would be able to shed some light on the mechanism of the underlying process – in contrast to most conventional analysis techniques, such as firing rates, correlations or inter-burst intervals, which provide little more than descriptive information. In search for measures able to capture more complex phenomena, over the last decade a new approach has been developed - pairwise maximum entropy modelling (MaxEnt). It is a statistical model that fits two sets of parameters to explain the probability of spiking patterns in the network: individual neuron parameters that could be interpreted as excitability; and pairwise interaction parameters that could be interpreted as the functional connection strength between neurons. Successful application of this model to a variety of recordings has helped reevaluate the importance of neuronal interactions in shaping network activity (Schneidman et al., 2006; Shlens et al., 2006). Additionally, the shortcomings of MaxEnt in certain cases can serve as an indicator of higher-order interactions between neurons (Ohiorhenuan et al., 2010). In present work we examine the extent to which the statistics of MaxEnd model fits and parameters can assist in understanding different modes of activity of a neuronal culture – specifically, along the duration of a homeostatic experiment. Neural activity from primary neuron cultures was recorded with the 4096 channel Active Pixel Sensor (APS) MEA, allowing for reliable isolation of single unit activity at near-cellular resolution (Berdondini et al., 2009). 20-minute datasets were obtained at different stages of homeostatic compensation during and after long-term CNQX application. For the data sets with a stationary activity state, large numbers of four-unit MaxEnt models were constructed for randomly chosen neurons on two spatial scales. Comparison of the statistics of the fits and parameters across the scales and across conditions indicates that different activity modes exhibit different profiles of local clustering and higher-order interactions.

High density MEAs provides improved capabilities in spatially and temporally resolving network activity patterns at the resolution of single cell, becoming more and more a standard technology in unravelling neuronal signal processing. In combination with fluorescence imaging, these devices open new perspective in finely identify structural and functional network properties. In this paper we present an automated analysis able to correlate the network topology extracted from fluorescence imaging of specific sub populations (e.g. inhibitory neurons), with high density electrophysiological recordings, paving the way to finely correlate functional activity with morphological spatial composition of dissociated neuronal cultures.

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We used electrophysiological signals recorded by CMOS Micro Electrode Arrays (MEAs) at high spatial resolution to estimate the functional-effective connectivity of sparse hippocampal neuronal networks in vitro by applying a cross-correlation (CC) based method and ad hoc developed spatio-temporal filtering. Low-density cultures were recorded by a recently introduced CMOS-MEA device providing simultaneous multi-site acquisition at high-spatial (21 μm inter-electrode separation) as well as high-temporal resolution (8 kHz per channel). The method is applied to estimate functional connections in different cultures and it is refined by applying spatio-temporal filters that allow pruning of those functional connections not compatible with signal propagation. This approach permits to discriminate between possible causal influence and spurious co-activation, and to obtain detailed maps down to cellular resolution. Further, a thorough analysis of the links strength and time delays (i.e., amplitude and peak position of the CC function) allows characterizing the inferred interconnected networks and supports a possible discrimination of fast mono-synaptic propagations, and slow poly-synaptic pathways. By focusing on specific regions of interest we could observe and analyze microcircuits involving connections among a few cells. Finally, the use of the high-density MEA with low density cultures analyzed with the proposed approach enables to compare the inferred effective links with the network structure obtained by staining procedures.

Finding the electrical conductivity of tissue is highly important for understanding the tissue’s structure and functioning. However, the inverse problem of inferring spatial conductivity from data is highly ill-posed and computationally intensive. In this paper, we propose a novel method to solve the inverse problem of inferring tissue conductivity from a set of transmembrane potential and stimuli measurements made by microelectrode arrays (MEA). We first formalize the discrete forward model of transmembrane potential propagation, based on a reaction–diffusion model with an anisotropic inhomogeneous electrical conductivity-tensor field. Then, we propose a novel parallel optimization algorithm for solving the complex inverse problem of estimating the electrical conductivity-tensor field. Specifically, we propose a single-step approximation with a parallel block-relaxation optimization routine that simplifies the joint tensor field estimation problem into a set of computationally tractable subproblems, allowing the use of efficient standard optimization tools. Finally, using numerical examples of several electrical conductivity field topologies and noise levels, we analyze the performance of our algorithm, and discuss its application to real measurements obtained from smooth-muscle cardiac tissue, using data collected with a high-resolution MEA system.

The fabrication of three-dimensional platinum nanoprotrusions by ion beam induced deposition (IBID) is here proposed to study their interaction with cultured rat primary neurons. The broad versatility of IBID allows to test the effects on cells network morphology of different protrusions shapes, from straight pillars to nail-headed or sphere-headed vertical structures. A preferential adhesion of cells on fabricated Pt nanostructures is clearly shown by fluorescence and scanning electron microscopy, with dense and suspended neuritic networks observed on arrays of large-headed pillars. This technique could be exploited to improve cells/electrodes adhesion and to increase the detected extracellular electric signal.

Microelectrode arrays (MEAs) are employed to study extracellular electrical activity in neuronal tissues. Nevertheless, commercially available MEAs provide a limited number of recording sites and do not allow a precise identification of the spatio-temporal characterization of the recorded signal. To overcome this limitation, high density MEAs, based on CMOS technology, were recently developed and validated on dissociated preparations (Berdondini et al. 2009). We show the platform capability to record extracellular electrophysiological signal from 4096 electrodes arranged in a squared area of 2.7 mm x 2.7 mm with inter-electrode distance of 21 µm at a sampling rate of 7.7 kHz/electrode. Here, we demonstrate the performances of these platforms for the acquisition chemically evoked epileptiform activity from brain slices. Moreover the high spatial resolutions allow us to estimate the effect of drugs in spatially modulating Inter-Ictal ((I-IC) activity.

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Recent advances in neural recording techniques open exciting possibilities of better understanding whole populations of neurons. Devices such as APS MEA (Active Pixel Sensor Microelectrode Array) [1, 2] allow for simultaneous recordings from 4096 channels (64x64 grid) at near-cellular resolution (electrode size: 21μm, electrode spacing: 42μm) and constitute a potentially very rich and detailed source of information on the dynamics of neural systems. Such volumes of data are however difficult to analyse: simple measures such as mean firing rates and correlations are often insufficient to capture interesting phenomena, while more sophisticated approaches can be computationally intensive and hard to interpret. Here we examine the applicability of pairwise maximum entropy (MaxEnt) [3–5] modelling to describe APS MEA data.

Our current understanding of the dynamics of neural circuits is limited by the poor resolution of multi-neuron recordings from large neural populations, which largely prevents the experimental verification of theoretical models and predictions. It is, for instance, difficult to distinguish between different potential classes of network architecture, such as feed-forward or recurrent networks, on the basis of simultaneous recordings from just tens of neurons. Recent advances in electronics have now made it possible to simultaneously record from thousands of neurons.

In this work, we investigate the spontaneous bursting behaviour expressed by in vitro hippocampal networks by using a high-resolution CMOS-based microelectrode array (MEA), featuring 4096 electrodes, inter-electrode spacing of 21 µm and temporal resolution of 130 µs. In particular, we report an original development of an adapted analysis method enabling us to investigate spatial and temporal patterns of activity and the interplay between successive network bursts (NBs). We first defined and detected NBs, and then, we analysed the spatial and temporal behaviour of these events with an algorithm based on the centre of activity trajectory. We further refined the analysis by using a technique derived from statistical mechanics, capable of distinguishing the two main phases of NBs, i.e. (i) a propagating and (ii) a reverberating phase, and by classifying the trajectory patterns. Finally, this methodology was applied to signal representations based on spike detection, i.e. the instantaneous firing rate, and directly based on voltage-coded raw data, i.e. activity movies. Results highlight the potentialities of this approach to investigate fundamental issues on spontaneous neuronal dynamics and suggest the hypothesis that neurons operate in a sort of 'team' to the perpetuation of the transmission of the same information.

Based on experiments performed with high-resolution Active Pixel Sensor microelectrode arrays (APS-MEAs) coupled with spontaneously active hippocampal cultures, this work investigates the spatial resolution effects of the neuroelectronic interface on the analysis of the recorded electrophysiological signals. The adopted methodology consists, first, in recording the spontaneous activity at the highest spatial resolution (interelectrode separation of 21 μm) from the whole array of 4096 microelectrodes. Then, the full resolution dataset is spatially downsampled in order to evaluate the effects on raster plot representation, array-wide spike rate (AWSR), mean firing rate (MFR) and mean bursting rate (MBR). Furthermore, the effects of the array-to-network relative position are evaluated by shifting a subset of equally spaced electrodes on the entire recorded area. Results highlight that MFR and MBR are particularly influenced by the spatial resolution provided by the neuroelectronic interface. On high-resolution large MEAs, such analysis better represent the time-based parameterization of the network dynamics. Finally, this work suggest interesting capabilities of high-resolution MEAs for spatial-based analysis in dense and low-dense neuronal preparation for investigating signaling at both local and global neuronal circuitries.

This paper presents a chip-based electrophysiological platform enabling the study of micro- and macro-circuitry in in-vitro neuronal preparations. The approach is based on a 64 × 64 microelectrode array device providing extracellular electrophysiological activity recordings with high spatial (21 µm of electrode separation) and temporal resolution (from 0.13 ms for 4096 microelectrodes down to 8 µs for 64 microelectrodes). Applied to in-vitro neuronal preparations, we show how this approach enables neuronal signals to be acquired for investigating neuronal activity from single cells and microcircuits to large scale neuronal networks. The main elements of the platform are the metallic microelectrode array (MEA) implemented in Complementary Metal Oxide Semiconductor (CMOS) technology similar to a light imager, the in-pixel integrated low-noise amplifiers (11 µV_rms) and the high-speed random addressing logic. The chip is combined with a real-time acquisition system providing the capability to record at 7.8 kHz/electrode the whole array and to process the acquired signals.

The spike represents the fundamental bit of information transmitted by the neurons within a network in order to communicate. Then, given the importance of the spike rate as well as the spike time for coding the activity generated at the level of a cell assembly, a relevant issue in extracellular electrophysiology is the correct identification of the spike in multisite recordings from brain areas or neuronal networks. In this paper, we present a novel spike detection algorithm, named Precise Timing Spike Detection (PTSD), aimed at (i) reducing the number of false positives and false negatives, in order to optimize the rate code, and (ii) improving the time precision of the identified spike, in order to optimize the spike timing. The PTSD algorithm considers consecutive portions of the signal and looks for the Relative Maximum/Minimum whose peak-to-peak amplitude is above a defined differential threshold and responds to specific requirements. To validate the algorithm, the presented spike detection has been compared with other methods either commercially available or proposed in the literature by using two benchmarking procedures: (i) visual inspection by a group of experts of a portion of signal recorded from a rat cortical culture and (ii) detection of the spikes generated by a realistic neuronal network model. In both cases our algorithm produced the best performances in terms of efficiency and precision. The ROC curve analysis further proved that the best results are reached by the application of the PTSD.